In commercial adult managed care populations, typically 10-20% of the population will incur 80-90% of the risk exposure for the risk-bearing payer. A major challenge in today's managed care environment is prospectively predicting which members are at risk for high utilization to enable appropriate targeting of proactive case management and intervention programs. These members could enjoy better health and incur fewer health-related expenses if they could be identified early and managed proactively.
Historically over the last 30 years, health assessment tools for adult populations (18-65 years of age) have been built upon clinical risk/chronic disease models to guide clinical decision making, to serve as outcome measures, or to automate aspects of care such as automated problem lists, laboratory results, and healthcare reminders. The emphasis has been on practical clinical issues, resulting in limited predictive power in terms of targeting those at risk for higher levels of healthcare-seeking behavior.
Increasingly, the ability of risk-bearing payers to approach population health management in a cost and quality-effective manner, will be dependent upon sound predictive models which guide care toward members who have a higher probability of becoming a high care user in the near-term. Adding momentum to the efforts, technological advances such as ASP and dynamic HTML Web tools, widespread connectivity to the Internet, and digital, variable-data print technologies have only recently become available to make population-based care management a real option versus a rhetorical question.
Theoretically and empirically, evidence suggests that near-term healthcare utilization may not be as strongly tied to traditional disease and clinical risk models as once thought. Indeed, 40-60% of office visits are often tied to non-disease based concerns. Also, from a practical perspective, risk/disease models often miss key prescriptive data which may guide the health counseling process (i.e., areas of readiness to change, stress levels, levels of perceived self-management, etc.) and key outcome measures (i.e., improvements in functional ability, levels of stress emotions, disease and medication compliance, etc.). In a practical sense, claims-based stratification methods are limited by the inherent inadequacies of the claims data itself, including the lack of claims history from new enrollees and the several month time lags often required to access and process the predictive information.
Therefore, a need exists for a healthcare management system and method that identify, based upon easily ascertained self-reported information, those members of a managed healthcare group who are at risk for high near-term healthcare use